Search Results - (( java optimization modified algorithm ) OR ( basic attack detection algorithm ))

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  1. 1
    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System

    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

    Published 2019
    “…As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. …”
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  2. 2
    Machine Learning Based Two Phase Detection and Mitigation Authentication Scheme for Denial-of-Service Attacks in Software Defined Networks

    Machine Learning Based Two Phase Detection and Mitigation Authentication Scheme for Denial-of-Service Attacks in Software Defined Networks by Najmun, Najmun

    Published 2024
    “…In this research, Two-Phase Authentication of Attack Detection (TPAAD) scheme is proposed and investigated for detection and mitigation of DoS attacks in SDN to increase the performance of the above-mentioned issues. …”
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  3. 3
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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  4. 4
    A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system

    A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system by M.H., Ali, Mohamad, Fadlizolkipi, Ahmad Firdaus, Zainal Abidin, Nik Zulkarnaen, Khidzir

    Published 2018
    “…There are several limitations that facing intrusion-detection system in current days, such as high rates of false positive alerts, low detection rates of rare but dangerous attacks. …”
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  5. 5
    Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection

    Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection by Ghanem, Waheed Ali Hussein Mohammed

    Published 2019
    “…Considering the wide success of swarm intelligence methods in optimization problems, the main objective of this thesis is to contribute to the improvement of intrusion detection technology through the application of swarm-based optimization techniques to the basic problems of selecting optimal packet features, and optimal training of neural networks on classifying those features into normal and attack instances. …”
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  6. 6
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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  7. 7
    Image watermarking optimization algorithms in transform domains and feature regions

    Image watermarking optimization algorithms in transform domains and feature regions by Tao, Hai

    Published 2012
    “…Unfortunately,watermark resistance to geometric attacks is the most challenge work in traditional digital image watermarking techniques which causes incorrect watermark detection and extraction. …”
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  8. 8
    Website assurance monitoring application with MD5 Hashing and SHA-26 Algorithm / Abdullah Sani Abd Rahman and Samsiah Ahmad

    Website assurance monitoring application with MD5 Hashing and SHA-26 Algorithm / Abdullah Sani Abd Rahman and Samsiah Ahmad by Abd Rahman, Abdullah Sani, Ahmad, Samsiah

    Published 2022
    “…The Website Assurance Monitoring application has been developed based on MD5 hashing technique and SHA26 algorithm. By using Microsoft Visual Basic 6.0, the Graphical User Interfaces (GUIs) of the application to provide an easy or user-friendly monitoring application. …”
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  9. 9
    An enhance embedding method using edge and textures detection for image steganography

    An enhance embedding method using edge and textures detection for image steganography by Al-Maliki, Alaa Jabbar Qasim

    Published 2024
    “…By embedding in noisy zones using edge and texture detection which complicates feature extraction, making hidden information more secure and statistically less detectable than basic LSB matching techniques. …”
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  10. 10
    Incremental learning for large-scale stream data and its application to cybersecurity

    Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

    Published 2015
    “…My Ph.d is focusing on constructing a detection system of malicious spam email and DDoS attack using a large number of dataset which are obtained by a server that collect double-bounce email and darknet for malicious spam email detection system and DDoS backscatter detection system, respectively. …”
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  11. 11
    Enhancement of network security by use machine learning

    Enhancement of network security by use machine learning by Hasan, Ahmed Raheem

    Published 2019
    “…The ANNDL uses iterations method to detect the data, do matching and identify the data. …”
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  12. 12
    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs

    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

    Published 2019
    “…Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the rate severity of fault detection per unit test cost. …”
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